Category: Technology

Recently Twilio released a new product called Studio, which is a graphical drag and drop editor you can use to build workflows, IVR’s and bots – both via voice and SMS channels. Over the last little while, I’ve been using Studio to build some awesome bots.

One of the bots that I have built is a blacklisting tool that prevents my apps from spamming and messaging unwanted users, this tool uses another product of Twilio called ‘functions’ which is essentially AWS Lambda but executed within the Twilio ecosystem.

To do this project I found a great service called BanishBot, which dub’s itself as ‘An API designed to help you manage opt-in/out communications and access lists.’ In short, its an API you can use in distributed systems to manage your opt-in and out lists.

In this post, I’m going to use Studio, Functions, and BanishBot to create an auto-updating opt-in/out application. At the end of the build, I’ll have a system where customers/people can automatically opt-in or out of my messages. I’ll then be able to use BanishBot in my application development as the last mile checker to make sure I’m not actually sending out unwanted messages.

You’ll need the following to do this project:
Twilio account (upgraded and with a phone number) – Sign up here
BanishBot account – Sign up here

Once your setup with your accounts, go into Twilio functions and create a new function using the blank template.
Name the function something like ‘BanishNewNumber’, then copy and paste this code into the code field – update the username and APIKey fields with your own credentials.

Once you have set this function up click save.
Make the second function, called ‘UnbanishNumber’ then copy and paste this code into the code field – update the username and APIKey fields with your own credentials.

But why build an opt-out bot?
When engaging with consumers, customers or people via SMS you have to comply with carrier compliance requirements, industry standards, and applicable law.
These often include the keywords HELP and STOP. In the case of shortcodes (five/six-digit numbers), you are also required to manage your own blacklist – something BanishBot is designed to do.

This project is going to conform to the highest of standards, that is we are going to manage the following keywords:STOP, END, CANCEL, UNSUBSCRIBE, QUIT, HELP, INFO, START, and SUBSCRIBE

I’m choosing to adhere to the highest of standards because it means I can use this project/code in a shortcode application without changing any code – handy!

The keywords are broken down into three areas; Stop, Start and Help.
When a user sends a message containing one of the STOP words (STOP, END, CANCEL, UNSUBSCRIBE, QUIT), we are going to banish this number.
When a user sends a message containing one of the START words (START, and SUBSCRIBE), we are going un-banish the number.
When a user sends a message containing one of the HELP words ( HELP, INFO) we are going to reply to the user with details on how they can get in touch – an email address or website for example.

From your Twilio console navigate to Studio and create a new flow, I’ve called mine ‘MatBot’ but feel I recommend you stick with a naming convention relevant to your project.

Your new empty flow will contain just a red trigger box with three connectors attached, one for inbound messages, one for inbound calls and one for REST API requests.

If you aren’t familiar with Studio have a look at the getting started pages.

Today we will be focusing on the SMS opt-out mechanism.
The first ‘widget’ we are going to drag onto the page is the ‘Spit’ Widget. A split is used to help guide how an application will react given different input parameters – in our case the SMS message body.
Drag a split widget onto the flow and link it up to the inbound SMS trigger. It should look something like this:

Now when someone sends our number a message, we are going to parse that text and perform different responses based on the message body.

Before we can add any connects let’s drag the rest of the components onto the flow and configure them.
Drag a function widget onto the flow and name it ‘BanishThisNumber’, then from the function drop down select the function ‘BanishNewNumber’ and in the parameters section create a new parameter called ‘NumberToBanish’ with a value of ‘{{trigger.message.From}}’. The {{ brackets }} tells studio that this parameter is dynamic and to use the SenderID we received the message from.

Once you have this widget setup save and then repeat the function setup, this time for the unbanish number function. I called mine ‘UnBanishThisNumber’ linking to function ‘UnbanishNumber’ passing a parameter called ‘numberToUnBanish’ with value ‘{{trigger.message.From}}’.

Great! Now let’s add our first split, click the ‘New’ button, select ‘Regex’ from the drop down and in the value box type STOP. Once you have typed STOP a new drop-down will appear. From the drop-down select the function ‘BanishThisNumber’.

Now when someone sends the message STOP to your number, the studio flow will route the message to the ‘BanishThisNumber’ function which will update the BanishBot service with this number which will now be banished. Now that you have one link setup lets connect the other opt-out keywords; END, CANCEL, UNSUBSCRIBE and QUIT.

Now, this is great but what if a user wants to opt back into your service. To opt a user back in they need to send one of the following words; START or SUBSCRIBE. You can add these words the same way you added the STOP keywords but link the keywords to the function ‘UnBanishThisNumber’.

Finally, we need to add support for the help/information keywords; HELP, INFO.
For help and information, we can reply to the message with a response that directs the user to our helpdesk or email address.
Drag a ‘Send Message’ Widget onto the flow and insert your response message.

Your Studio flow should look something like this:

Fantastic! Congratulations on building your opt-out bot!
Now when anyone wants opt-out of your services they will be handled automatically by the studio flow.

For the sending side, you now need to build BanishBot into your sending mechanism, so that each time you want to send someone an SMS message, the sending mechanism first checks the number against BanishBot and rejects the request if the number is indeed banished. – You can find the BanishBot Docs here, https://www.banishbot.com/docs.html
Hope you have fun banishing things 😉

An ever increasing problem in the digital age is the continual use and unwanted exposure of rude words and profanity. Thinking about this from a business perspective its never great when your customer service agents are exposed to an angry customer who does nothing but swear in a real time live chat.

Lets take for example a (Twilio) SMS powered customer support channel.

The standard work flow would be

Raw Inbound Request

Here the message comes into your support application, the raw content is added to a ticket or messaging system and is presented to your customer agent.

If this inbound contains profanity or other rude words your agents are immediately exposed to this.

Fortunately I’ve found a service that scans text bodies for rude words and replaces them – WebSanitize.

Using WebSanitize you can implement a profanity filter at the server or application layer.

This lets you build a workflow into your inbound messages process.

In layman’s terms we can add a method to scan inbound messages for profanity and replace the words.

This gives you the option of adding a layer between the rude inbound messages and our / your customer service agents.

Getting started with WebSanitize

The core of WebSanitize is fast API, to get started you need to sign up for an account with them. Once you have an API Key the request is fairly straight forward:

The first thing to notice is that ‘MessageAlert‘ flag has been set to true. You can use this as a first step to see if you need to replace the original text is to check this status. e.g.:

if(MessageAlert == true){
// Replace the original text using the returned variable CleanerMessage
} else {
// The original message did not contain any profanity
}

With this ‘Sanitized’ message replacing the original one we can present this to our customer support agent.

Why use WebSanitize or any kind of screening service?
It’s often said that a companies best asset is the people.
If you think about the abuse and language thats occasionally used by irate people when they speak to customer service agents, any barrier or in this case a ‘Sanitizer’ that can shield an employee from those harsh words is always a good thing.
Using a service like WebSanitize gives your customer service agents confidence that you or your organisation are proactively taking steps to keep them safe and shielded from those undesirable words.

At Amazon AWS ReInvent 2016 one of the cool new features that was released was Polly, an amazingly slick synthetic voice engine. At time of writing, Polly supports a total of 47 male & female voices spread across 24 languages.

From the moment I saw the demo I knew I could use this to generate on the fly audio for use on Twilio.

Using AWS Polly with Twilio will allow you to make use of multiple languages, dialects and pronunciation of words. For example the word “live” in the phrases “I live in Seattle” and “Live from New York.” Polly knows that this pair of homographs are spelled the same but are pronounced quite differently. (sic) .

In this example I’m going to be using AWS Polly, Twilio TwiML (the Twilio XML based markup language) and NodeJS to produce an app that will allow you to generate on demand MP3 files which can then be nested in TwiML <Play> verbs.

Create a new use that has programatic access (this will generate keys and a key secret).

Attach the user to “AmazonPollyFullAccess” policy and finish the account creation steps.

Phase Two: Create a new NodeJS project

In terminal navigate to where you keep your projects and create a new directory

mkdir nodeJSPollyTwiML && cd nodeJSPollyTwiML

Inside the directory initialise node using

npm init

The initialise script will ask you some basic questions about the application; name, keywords etc. I will leave this up to you.

Once the initialise script has finished we can install the required modules needed by NodeJS to run our application.

npm install --save aws-config aws-sdk body-parser express forms

This will install the required modules needed to build and run this app.
Now we can begin to build out this application.
Call up your favourite text editor – mine currently is Atom, which is made by the GitHub team.

Atom allows you to keep a project directory on the left for easy navigation as well as colour coding all the files based on their git state.

The structure of the app is going to be:

├── server.js
| ├── config.js
├── audioFiles

server.js will be responsible for all the application processing

config.js will be where the system configuration files will be stored

audioFiles will house the saved audio records.

Before we can write any server code we need somewhere to store our AWS credentials. Create a new file called config.js, add to this file:

In the configuration page we have broken out the settings into two parts; one for production systems and one for test systems (default in this case). As we are still building this app I will be working from the test environment.

Using Module exports we can now call the config file into server.js and load the credentials when we need them!

Open server.js file and load the modules needed, this should be the same as what was in package.json after npm install had completed.

Breaking this down, the path is comprised of ‘play‘ this refers to the Twilio Verb Play, if the application were to be built out further you could use other verbs or commands to define other paths, e.g. /host/ could generate the audio file but return the URL path of the audio file, letting the application host the file.

Next ‘Carla‘ refers to the Polly voice we want to use, as mentioned before AWS polly has a total of 47 male & female voices. Each of these voices has a name so its easy to reference which voice you want to use by calling that name.

The last part: ‘Hi%20Mathew.%20this%20is%20Carla%20from%20Amazon%20Web%20services.‘ Is the message that needs to be converted into speech. To ensure that the message is transmitted correctly you will need to URL encode the string, this converts spaces into %20, you can find more details on URL encoding here.

When a HTTP GET requests comes in that starts /play/ this function will be called. Next voiceID is the variable for the the Polly voice requested, and textToConvert the URL encoded text that needs to be converted.

To make the request to AWS Polly, the pollyParameters object needs to be populated, this consists of the chosen voice and the text to convert. MP3 has been fixed in this example.

Now the application is ready to call Polly,

polly.synthesizeSpeech(pollyParameters, pollyCallback);

here the app passes the parameters as well as a callback that will be invoked when the job is finished.

Once Polly has finished generating the audio file it will run the callback and (if successful) pass back the audio file.

The callback pollyCallback is now responsible for a two things; saving the file to disk and passing the file back to the users request.

Now we have a HTTP addressable endpoint we can integrate our audio files into Twilio’s TwiML, when Twilio makes a request to your Twilio application for TwiML you can now integrate this application into <Play> verbs. An example is:

When you compile your TwiML you will need to make sure that the text to speak has been url-encoded, otherwise Twilio will fail the TwiML for not being compliant.GitHub: https://github.com/dotmat/nodeJSPollyTwiML

Conclusion:

Integrating AWS Polly into your Twilio apps is now fast and easy. With a HTTP request you can request a desired voice convert text into an audio file which can be used with Twilio to play back to a caller / customer.

Betterments:

At present time, the application is unsecured, anyone with access to your app / domain could quickly start using polly to increase your AWS spend. While Polly is very cheap ($0.000004 per character, or about $0.004 per minute of generated audio) its still something that should be addressed. I would recommend implementing some kind of auth that can check against known users database (Basic Auth for example)

For common messages that you might use over and over its pointless to keep generating this for one time use, if you had a prebuilt database of common messages that your application uses, you could reference these from the API. In the GitHub repo I expanded the code to allow you to pull audio files by calling the MP3 file name.

The app currently does no house keeping of audio files, each time you make a request to Polly it will generate an audio file. A good tool next would be something that deletes audio files over than a X days or weeks.

I hope this tutorial has been helpful, please reach out if you have any issues or questions!

For example, your at an event, ‘CloudForce’ for example 😉 and you want to ask your guests about the experience they are having as well as capture the guests phone number in a lead campaign in Salesforce ready to pick up with the lead after the event. This gives you instant feedback on how people are enjoying the event, an incoming lead stream and verified phone numbers from potential customers.

In this post, Im going to build on using Twilio cookies to populate a lead campaign in Salesforce. To initiate this setup, I want to get the interested lead to message a keyword “CloudForceEU” for example. Once the initial message comes in, I want to ask 4 questions to the lead and then pass the captured data to our Salesforce instance.

A Salesforce instance where you can add custom lead fields and use Web2Lead Form generator

A PHP based server to host the script found on my Github. – If your savy you can make your own in another language such as Ruby or Python 🙂

Setting up Salesforce

To begin we need to add 4 custom fields to our salesforce lead’s panel. As I am choosing to ask 4 questions to our potential lead I want to capture this information so I can get an overall feel for the event as well as capturing info about our potential lead.

To add a custom field in Salesforce go to : salesforce.com/p/setup/layout/LayoutFieldList?type=Lead&setupid=LeadFields

or: Setup > Leads > Fields and scroll to the bottom for ‘Custom Fields’ It should look something like:

Leads Generation SalesForce

Here we want the button marked ‘New’. Following the steps, we want a new text box of no more than 150 (This is WAY more than we need as we are only gathering simple responses). Fill in the details for the new field and then continue along. I tend to add the details of the question in the description so that I know what Question1 relates to. Continue this until you have all your question fields added.

Now we are going to build our Web2Lead form and capture the ID’s needed for our SMS Script.

Navigate to: Customize > Leads > Web-To-Lead

Remove all the initial fields from the box marked ‘Selected Fields’ and then import:

PhoneNumber

Campaign

Question1

Question2

Question3

Question4

You could add first name to this setup but you would need add a name collection to the SMS conversation, while its easy to do. Its not something I will be doing in this setup.

In the end your setup should look something like:

SF Web2Lead

Then click generate. Salesforce will spit you out some code that you could use in a webform but we are going to grab the details of this code and use it in our SMS lead tracker. The code will look something like:

<?php
// Load the questions we want:
$question1 = ‘Hello. Welcome to the event! We would like to ask you some questions about your experience.</Message><Message>What did you think of the venue & refreshments? 5 (Exceptional) 0 (Poor)’; // by adding the </Message><Message> you can break up the initial response into a welcome message and then question1.
$question2 = ‘And the content of the Presentations? 5 (Exceptional) 0 (Poor)’;
$question3 = ‘How likely are you to attend future Twilio events from 5 (Definitely would) to 0 (definitely would not)’;
$question4 = ‘Is there anything specific you would like to discuss with Twilio? 5 (Yes, please asks someone to call) 0 (I’ll contact you if I need anything)’;
// After we have all 4 questions we can upload to the DB and thank the user for their input
$endStatement = ‘Thanks for your time. Hope you have a fun day!’;

// If we have no cookies we need to set all the cookies to nil and ask the opening question.
if(!isset($_COOKIE[‘question1’])) {
$TwiMLResponse = $question1;
//setcookie(‘question1’, ‘nil’);
setcookie(‘event’, $_POST[‘Body’]);
setcookie(‘question1’, ‘nil’);
setcookie(‘question2’, ‘nil’);
setcookie(‘question3’, ‘nil’);
setcookie(‘question4’, ‘nil’);
}
// If Question 1 is blank we can pair the answer to question 1
elseif ($_COOKIE[‘question1’] == ‘nil’) {
setcookie(‘question1’, $_POST[‘Body’]);
$TwiMLResponse = $question2;
}
// If Question 1 is not blank we find out if question 2 is blank and move up the ladder
elseif (($_COOKIE[‘question2’] == ‘nil’)) {
setcookie(‘question2’, $_POST[‘Body’]);
$TwiMLResponse = $question3;
}
elseif (($_COOKIE[‘question3’] == ‘nil’)) {
setcookie(‘question3’, $_POST[‘Body’]);
$TwiMLResponse = $question4;
}
// After we get the response for question 4, we can assign it to the question.
// Now we have all 4 questions answered and can pass the thank you note and also make a HTTP POST to our end point
elseif (($_COOKIE[‘question4’] == ‘nil’)) {
// With the last question answered, we can reply with our end statement and POST all the data from the conversation.
$TwiMLResponse = $endStatement;
// So now we have the cookies for the event and questions 1 to 3 and the BODY tag for answer 4. Now we can make a POST request to our form with that data.

As you can see Im only use one script to manage the conversation, updating the cookies and working out where the data needs to be updated to and eventually POSTed too. As we are capturing questions about our SalesForceEU event Im going to need 4 questions:

$question1 = ‘Hello. Welcome to the event! We would like to ask you some questions about your experience.</Message><Message>What did you think of the venue & refreshments? 5 (Exceptional) 0 (Poor)’; // by adding the </Message><Message> you can break up the initial response into a welcome message and then question1.
$question2 = ‘And the content of the Presentations? 5 (Exceptional) 0 (Poor)’;
$question3 = ‘How likely are you to attend future Twilio events from 5 (Definitely would) to 0 (definitely would not)’;
$question4 = ‘Is there anything specific you would like to discuss with Twilio? 5 (Yes, please asks someone to call) 0 (I’ll contact you if I need anything)’;

As the script gets more replies from Twilio it populates the questions cookies until they are all full of data. Then we thank the user for their time, assemble the POST request and send it off to SalesForce.

As you can see this opens up lots of possibilities of lead capture and accurate number sourcing from events. You can even have a campaign manager back at HQ reaching back out to leads while they are still at the event.

When I talk to people about using Twilio (Twilio.com) SMS to engage with their customers I get a lot of push back on the technical side of how to manage a two way conversation.

If you think back to the old days before iPhones and threaded messages. We had whats now called ‘Nokia’ style messages. This is just a continuous list of messages that arrive into your mailbox, messages between two handsets were not threaded or connected in anyway. Twilio operates in the same fashion, a message out to a handset is not connected to a message in from a handset, there is no ID that links them and no function to make parent child relationships.

Step in Twilio Cookies. In the internet world you can use cookies to track a clients events and navigation throughout your website, you can use it to log if a customer viewed a product or read an article and then clicked on a related one. A visual example :

Cookies Example (Taken from Twilio.com)

Using Cookies with Twilio we can imitate a conversation with the handset / end user, collecting data along the way and at the end of the conversation we can do something meaningful with the information – such as POST the data to a database or store in a file somewhere.

Why would conversations / cookies be handy to use with Twilio? Well, imagine your hosting an event and you want to get feedback from your guests, you can pass your guests a Twilio powered phone number which when they message will initiate a conversation with them. At the end of the conversation we will have meaningful answers about the event, not to mention the guests phone number so we can follow up with that all important sales call!

The example Im using here is a single page PHP powered script that when your guests message will ask them favourite colour, meal, drink and if they want to go to the movies next week. We will then take this data and make a HTTP POST request to any server with the data. You can use Google Forms here to capture all your responses by amending the URL and POST ID’s. See: https://www.twilio.com/blog/2012/11/connecting-twilio-sms-to-a-google-spreadsheet.html as an example here.

These are the questions we want to ask when the user sends us a message:

// Load the questions we want:
$question1 = ‘Hello. What is your favourite colour’;
$question2 = ‘Thanks! Whats your favourite meal’;
$question3 = ‘Tasty! What about to Drink?’;
$question4 = ‘Delish! Do you want to go to the movies next week?’;

At the end of our conversation we want to thank the user so they know that no more questions are coming, and its polite!

// After we have all 4 questions we can upload to the DB and thank the user for their input
$endStatement = ‘Thanks for your time. Hope you have a fun day!’;

We are going to use cookies to track the conversation, I find that its better to pass all the cookies info we want with nil values and add data to these values as the conversation goes rather than adding them as we go. This way we can logically track the conversation.

As this conversation is kicked off by the user and not by us, we set all the question cookies to nil, load the first question into $TwiMLResponse and set the event cookie to be the current data in the original message. So for example if our event was called ‘Mats BBQ’ and I asked all my users to send the opening message as ‘Mats BBQ’ the event cookie would be ‘Mats BBQ’.

Because no question has been asked yet, all our cookie values are blank and we can ask our questions:

Questions 1 to 3 are the same, its a case of moving through the questions, assigning the ‘Body’ value to the last question asked. When we get to the answer for Question 4 we have all our answers, so no need to set any more cookies.

Now we can take all our data, wrap it up into an array and make a HTTP POST request with this data:

// After we get the response for question 4, we can assign it to the question.
// Now we have all 4 questions answered and can pass the thank you note and also make a HTTP POST to our end point
elseif (($_COOKIE[‘question4’] == ‘nil’)) {
// With the last question answered, we can reply with our end statement and POST all the data from the conversation.
$TwiMLResponse = $endStatement;
// So now we have the cookies for the event and questions 1 to 3 and the BODY tag for answer 4. Now we can make a POST request to our form with that data.

So for months of my development I’ve wanted to host and use a GitLab Droplet on Digital Ocean (https://www.digitalocean.com/?refcode=8dc34df963c1). Using the server works great for all my projects bar, ANYTHING xCode! The hours spent screaming at a bit of software “… But the Password is correct you stupid f*$ker, AHHH!!!!”

Up to this point, I had taken to using Xcode to locally manage the git repo for a particular project, which worked well but didn’t allow me to swap machines, pass code to friends or do anything close to what GitLab can to in term of backup and team management.

Eventually I came up with the idea of initialising the Git settings for a project and was going to push all the updated code / changes to GitLab using terminal. Ie, write all the code / project in Xcode, then close Xcode, launch terminal and then push from terminal to GitLab. But then, after I had setup a project directory, added the information needed to connect it to my git: “git init” and then “git remote add origin [email protected]:MyUserName/GitRepo.git” and had done the initial commit and push.

I went into Xcode, created a new project, navigated to the same directory and Xcode proudly told me that a git repo was already in use! See screenshot:

Git Repo Already Exists

Clicking ok, and moving on with the rest of the project settings I can now push, commit and update all information FROM Xcode to my GitLab projects.

I hope this helps lots of people who struggle with GitLab / Xcode interrogations, from looking at forums and how-to guides. It seems a lot of people current struggle.

One of the coolest things about having your own phone number with Twilio (Twilio.com). Is that you can do some very nifty things with it.

For example, if you need a conference room setup with a text message you can forward to your friends, you can configure this in a PHP script quite easily, we can even get the script to respond to your request with details about the number to ring and the pin code.

If you haven’t already done so, head over to Twilio (Twilio.com) an sign up at: Try-Twilio This will provision you a Twilio number that you can call into and interact with.

Once we have a number lets build a script that can respond to our SMS message, generate the conference room pin and then respond back with the details of our conference room.

Here in this script we are checking to see if the GET method has been passed with any digits, indicating that the caller has already been greeted and input a conference pin. If we have a conference pin then we can use the digits to put his caller into that conference room.

This is a very simple conference line setup tool, and it will need some further modifying to make it secure. For example right now any 4 digit numbers will create a conference room. You may wish to modify the code so that only generated pin numbers can be used and those numbers cannot be reused – entirely up to you.

So finally after many months of development, design, build, rebuild and writing of code. I have some data and information on my beehive! Its remarkable how much data you can get and learn about an environment by monitoring a few points over time. At the moment the BeeBetaBox is currently loading up, sending a HTTP POST to an end point with lots of data about the beehive every 10 mins. This data is rolled into a database and then assembled together to generate graphs showing temperature over time Because I capture the GPS co-ordinates of the hive as well we can then use this to make a lookup against a weather service and provide the weather predictions for the next 3 days. Handy if your hive is remote and you want to see about a visit soon!

So after spending a considerable amount of time using Sakis – the 3G dongle software. I feel I need to write a better post on how to connect your Debian based system / Raspberry Pi to the internet using a 3G connection.

– Be warned, Data on the 3G network is RARELY all you can eat, ensure you have an appropriate data plan before you let your Pi loose.

– 2nd Warning.. 3G dongles consume huge quantities of power, if your running the project off a battery setup, write some code to disconnect the 3G stick when not in use to prevent it from eating all the power just staying connected.

With that being said, to download and install the 3G software:

sudo apt-get -y update

then

sudo apt-get -y upgrade

then

sudo apt-get -y install ppp

Once the updates have taken route and ppp has been installed you will need to download sakis;

wget “http://www.sakis3g.org/versions/latest/armv4t/sakis3g.gz”

then

gunzip sakis3g.gz

then

chmod +x sakis3g

finally

sudo ./sakis3g –interactive

This will take you to the interactive window used by Sakis, you can configure the 3G dongle here and then issue the connect command. The software has been set out to be easy to interact with.

Depending on the Simcard you have in the dongle, it may attempt to download the latest settings automatically and then offer them to you as a connection method.

If you don’t get that luxury you can manually enter your APN, username and password manually in the prompts.

One feature that I LOVE about Sakis is that you can call the connect command from within another program, lets say my Pi gathers some data up and then needs to send it somewhere on the net, you can tell the program to connect to Sakis like this: